Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for providing search suggestions, the method comprising: capturing, by a computer, data from a first application, wherein the data includes data displayed on a screen by the first application; segmenting, by the computer, the data from the first application into blocks, wherein the blocks correspond to areas displayed on the screen; extracting, by the computer, words from the data from the first application; generating, by the computer, tokens of phrases and singular words from the extracted words using natural language processing tokenization; retrieving, by the computer, stored significance data for the first application, wherein the significance data includes information used for determining respective blocks that will be more likely to result in a search; scoring, by the computer, the blocks based upon the data from the first application, wherein the scoring of the blocks is further based upon the stored significance data; scoring, by the computer, the tokens based upon the scoring of the blocks and the data from the first application; detecting, by the computer, a switch action from the first application to a second application, wherein the second application contains a search function; and providing, by the computer, the scored tokens to the search function of the second application.
This invention relates to a method for generating and providing search suggestions based on content from a first application, which is then used in a second application with search functionality. The method involves capturing data displayed on the screen of the first application, such as text or other visual elements, and segmenting this data into distinct blocks corresponding to different areas of the screen. Words are extracted from these blocks, and natural language processing techniques are used to generate tokens from both individual words and phrases. The system retrieves stored significance data for the first application, which helps determine which screen areas are more likely to contain relevant search terms. The blocks and tokens are then scored based on their relevance, considering both the captured data and the significance data. When a user switches from the first application to the second application, the scored tokens are provided to the search function of the second application, enabling context-aware search suggestions. This approach improves search efficiency by leveraging content from the first application to enhance search queries in the second application.
2. The method of claim 1 , wherein the data captured from the first application further includes data on user behavior in the first application.
A system and method for capturing and analyzing user behavior data from a first application to enhance functionality in a second application. The technology addresses the challenge of integrating user interactions across different applications to improve user experience, personalization, or system efficiency. The method involves collecting data from the first application, including user behavior metrics such as interaction patterns, navigation paths, time spent on tasks, and input actions. This data is then processed to extract meaningful insights, which are used to inform or optimize operations in the second application. For example, user behavior data from a productivity tool could be used to customize settings or workflows in a collaboration platform. The system may also include mechanisms to ensure data privacy and security, such as anonymization or encryption, while maintaining the utility of the collected information. The approach enables seamless integration of user behavior analytics across applications, improving adaptability and responsiveness to user needs.
3. The method of claim 1 , wherein the data captured from the first application further includes characters a user selected in the first application.
A system captures and processes user interaction data from a first application to enhance functionality in a second application. The method involves detecting user actions within the first application, including text input, and transmitting this data to the second application. The second application then uses this data to perform operations such as auto-filling forms, generating content, or executing commands based on the captured input. The captured data includes characters selected by the user in the first application, allowing the second application to recognize and utilize specific text selections for tasks like context-aware suggestions or data transfer. This approach improves efficiency by reducing manual input and enabling seamless integration between applications. The system may also track additional interaction details, such as cursor movements or keyboard inputs, to refine the data processing. The second application processes the received data to determine relevant actions, ensuring accurate and timely responses based on the user's activities in the first application. This method enhances productivity by automating tasks and minimizing repetitive data entry across different software environments.
4. The method of claim 1 , wherein the data captured from the first application further includes one or more locations a user clicked in the first application.
This invention relates to user interaction tracking within software applications, specifically capturing and analyzing user behavior data to improve application functionality or user experience. The problem addressed is the lack of detailed insights into how users interact with applications, which limits the ability to optimize interfaces, identify usability issues, or personalize experiences. The method involves monitoring a first application to capture user interaction data, including the specific locations where a user clicks within the interface. This data is then processed to derive meaningful patterns or metrics, such as frequently accessed areas, navigation paths, or areas of confusion. The captured data may also include other interaction details like timestamps, dwell times, or input sequences. The system can correlate this click location data with application features or content to assess usability or engagement. For example, if users repeatedly click on non-interactive elements, it may indicate a design flaw. The method may also compare this data across different user groups or sessions to identify trends or outliers. The captured data can be used to generate reports, trigger automated adjustments in the application, or provide feedback to developers. The system may also integrate with other applications or services to enhance functionality, such as recommending content based on click patterns or adjusting interface layouts dynamically. The goal is to create a more intuitive and responsive application by leveraging real-time or historical user interaction data.
5. The method of claim 1 , wherein the data captured from the first application further includes one or more locations a user's cursor was located in the first application.
A system captures and analyzes user interaction data from a first application to improve user experience in a second application. The first application is a productivity tool, such as a word processor or spreadsheet, where user behavior is monitored to identify patterns, preferences, or inefficiencies. The captured data includes cursor movements, clicks, and other interactions, along with the specific locations where a user's cursor was positioned within the first application. This data is processed to extract insights, such as frequently accessed areas, navigation paths, or common editing behaviors. The insights are then used to customize or optimize the second application, which may be another productivity tool or a related software service. For example, the second application could adjust its interface layout, suggest shortcuts, or preload frequently used features based on the analyzed cursor data. The goal is to enhance efficiency and usability by leveraging observed user behavior from one application to improve another. The system may also include machine learning models to predict user needs or automate workflows based on the captured interaction patterns.
6. The method of claim 1 , wherein the stored significance data is stored on a remote computer.
A system and method for managing significance data in a distributed computing environment addresses the challenge of efficiently storing and accessing data that indicates the importance or relevance of information in various applications, such as machine learning, data analytics, or decision-making systems. The method involves generating significance data, which quantifies the importance of certain data points, features, or variables within a dataset. This significance data is then stored on a remote computer, allowing for centralized access, scalability, and collaboration across multiple users or systems. By storing the data remotely, the system ensures that significance metrics are consistently available, reducing redundancy and improving processing efficiency. The remote storage also enables real-time updates and synchronization, ensuring that all connected systems or users have access to the most current significance data. This approach is particularly useful in large-scale data processing environments where distributed computing resources are utilized, as it simplifies data management and enhances the reliability of significance-based decision-making processes. The method may also include additional steps such as retrieving, updating, or analyzing the stored significance data to support various applications, such as feature selection, model training, or data filtering.
7. The method of claim 6 , wherein the stored significance data is aggregated from more than one user.
The invention relates to a system for analyzing and aggregating user interaction data to determine the significance of digital content. The problem addressed is the need to assess the relevance or importance of digital content based on collective user behavior, rather than relying solely on individual interactions. The method involves tracking user interactions with digital content, such as clicks, views, or dwell time, and storing this data as significance data. The significance data is then aggregated from multiple users to generate a composite measure of content importance. This aggregated data can be used to rank, filter, or recommend content based on its perceived significance to a broader user base. The system may also apply weighting factors to different types of interactions or user demographics to refine the significance measurement. The aggregated significance data can be displayed to users or used by content management systems to prioritize or curate content. The invention improves content discovery and personalization by leveraging collective user behavior to determine content value.
8. The method of claim 1 , the method further comprising: updating, by the computer, the stored significance data based upon the segmentation of the data from the first application into blocks.
This invention relates to data processing systems that analyze and segment data from applications to improve information retrieval and management. The problem addressed is the need for more efficient and accurate segmentation of application data to enhance data processing tasks such as indexing, searching, and retrieval. Traditional methods often struggle with accurately dividing data into meaningful blocks, leading to inefficiencies in data handling. The method involves a computer system that processes data from a first application, where the data is segmented into blocks based on predefined criteria. The segmentation is performed to organize the data in a structured manner, making it easier to analyze and retrieve relevant information. The system stores significance data associated with each block, which indicates the importance or relevance of the segmented data. This significance data is used to prioritize or filter the data during subsequent processing steps. Additionally, the method includes updating the stored significance data based on the segmentation results. This ensures that the significance data remains accurate and reflects the latest segmentation of the application data. By dynamically updating the significance data, the system can adapt to changes in the data structure or content, improving the overall efficiency and accuracy of data processing tasks. This approach enhances the system's ability to handle large volumes of data from multiple applications while maintaining high performance and relevance.
9. The method of claim 1 , the method further comprising: monitoring, by the computer, one or more search terms entered by a user into the search function of the second application.
A system and method for enhancing user experience in digital applications involves monitoring user search behavior across multiple applications. The technology addresses the problem of fragmented search experiences, where users must repeatedly enter the same or similar search terms in different applications, leading to inefficiency and frustration. The method includes tracking search terms entered by a user in a first application and automatically applying those terms to a search function in a second application. This synchronization ensures consistency and reduces redundant input. Additionally, the method monitors search terms entered directly into the second application, allowing for bidirectional synchronization between the applications. The system may also analyze search patterns to refine recommendations or improve search accuracy. By integrating search functionality across applications, the technology streamlines user workflows and enhances productivity. The method is particularly useful in environments where users frequently switch between applications, such as productivity suites, enterprise software, or web browsers. The solution leverages computational resources to automate search term propagation, minimizing manual effort and improving user satisfaction.
10. The method of claim 9 , the method further comprising: updating, by the computer, the stored significance data based upon the search terms entered by the user into the search function of the second application.
This invention relates to a system for enhancing search functionality in a second application by leveraging significance data derived from a first application. The problem addressed is improving search relevance in the second application by utilizing user behavior and data from the first application, which may have more comprehensive or contextually relevant information. The method involves a computer system that stores significance data associated with search terms from the first application. When a user enters search terms in the second application, the system retrieves and applies this significance data to refine the search results. The significance data may include historical search patterns, user preferences, or contextual relevance metrics from the first application. By incorporating this data, the search function in the second application becomes more accurate and tailored to the user's needs. Additionally, the system dynamically updates the stored significance data based on the search terms entered in the second application. This ensures that the relevance of the search results continues to improve over time as the user interacts with the system. The method may also involve analyzing the user's behavior in the second application to further refine the significance data, creating a feedback loop that enhances search performance. The invention is particularly useful in environments where multiple applications share related data or where user behavior in one application can inform and improve functionality in another. By bridging the gap between the two applications, the system provides a more cohesive and efficient search experience.
11. The method of claim 1 , wherein the stored significance data includes prior determinations of blocks for the first application.
A system and method for optimizing application performance by analyzing and storing significance data related to code execution. The technology addresses inefficiencies in software execution where applications repeatedly process the same or similar code blocks without retaining insights from prior executions, leading to redundant computations and degraded performance. The method involves tracking and storing significance data for code blocks executed by a first application, where significance data includes prior determinations of which blocks are critical or frequently accessed. This stored data is then used to prioritize, optimize, or selectively execute certain blocks during subsequent runs of the same or related applications, reducing redundant processing and improving efficiency. The system may also apply this significance data to other applications with similar code structures or execution patterns, further enhancing performance across multiple software instances. The approach leverages historical execution data to dynamically adjust processing strategies, minimizing unnecessary computations and accelerating application performance.
12. The method of claim 11 , wherein the stored significance data includes significance values for blocks.
A system and method for analyzing and processing data blocks, particularly in digital signal processing or data compression applications, addresses the challenge of efficiently determining the importance or relevance of different data segments. The invention involves assigning significance values to individual data blocks, where these values indicate the relative importance or contribution of each block to the overall data set. These significance values are stored and used to prioritize, filter, or selectively process the data blocks based on their importance. The method may involve comparing significance values to a threshold to identify significant blocks, or ranking blocks for ordered processing. The significance values can be derived from statistical measures, signal strength, error metrics, or other criteria relevant to the application. This approach improves efficiency by focusing computational resources on the most important data, reducing processing time and memory usage. The system may include a processor configured to compute and store these significance values, and a memory module to retain the significance data for subsequent use. The method is applicable in fields such as image compression, audio processing, or any domain where selective data handling is beneficial.
13. The method of claim 11 , wherein the stored significance data includes a number of times a block has resulted in a search by a user.
A system and method for improving search efficiency by tracking and utilizing significance data related to user search behavior. The technology addresses the problem of inefficient search systems that do not adapt to user preferences or frequently accessed content, leading to slower retrieval times and suboptimal results. The method involves monitoring user interactions with searchable content, such as documents or data blocks, and storing significance data that reflects how often a particular block of content triggers a search. This significance data is then used to prioritize or optimize the retrieval of frequently accessed content, improving search performance. The system may also include a search interface that allows users to input queries and a processing module that analyzes the significance data to determine which content blocks are most relevant or frequently accessed. By dynamically adjusting search priorities based on historical usage patterns, the system enhances search speed and accuracy, particularly in large or complex datasets where manual optimization would be impractical. The method ensures that frequently searched content is prioritized, reducing latency and improving user experience.
14. A computer program product for providing search suggestions, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a computer to perform a method comprising the steps of: capturing, by a computer, data from a first application, wherein the data includes data displayed on a screen by the first application; segmenting, by the computer, the data from the first application into blocks, wherein the blocks correspond to areas displayed on the screen; extracting, by the computer, words from the data from the first application; generating, by the computer, tokens of phrases and singular words from the extracted words using natural language processing tokenization; retrieving, by the computer, stored significance data for the first application, wherein the significance data is based upon a likelihood that a user will be presented with content in respective blocks that will be more likely to result in a search and wherein the stored significance data includes prior determinations of blocks for the first application; scoring, by the computer, the blocks based upon the data from the first application, wherein the scoring of the blocks is further based upon the stored significance data; scoring, by the computer, the tokens based upon the scoring of the blocks and the data from the first application; and detecting, by the computer, a switch action from the first application to a second application, wherein the second application contains a search function; providing, by the computer, the scored tokens to the search function of the second application.
This invention relates to a system for generating search suggestions based on content displayed by an application. The system captures data from a first application, including text displayed on the screen, and segments it into blocks corresponding to different areas of the display. Words are extracted from these blocks and processed using natural language processing techniques to generate tokens of phrases and individual words. The system retrieves stored significance data for the first application, which indicates the likelihood that content in specific blocks will lead to a search query. Blocks are scored based on this significance data and the extracted content, and the tokens are then scored accordingly. When a user switches from the first application to a second application with a search function, the scored tokens are provided to the search function, enabling relevant search suggestions. The system improves search efficiency by prioritizing content more likely to be relevant to the user's search intent, reducing the need for manual input. The significance data is pre-determined based on historical usage patterns, ensuring that the most relevant content is highlighted. This approach enhances user experience by automating the search suggestion process and reducing the time required to formulate queries.
15. The computer program product of claim 14 , wherein the data captured from the first application further includes data on user behavior in the first application.
This invention relates to a computer program product for capturing and analyzing data from a first application to enhance functionality in a second application. The system captures data from the first application, including user behavior data such as interactions, usage patterns, and other activity metrics. This captured data is then processed and used to improve the performance, personalization, or other features of the second application. The system may involve real-time or batch processing of the data, depending on the requirements. The captured user behavior data helps tailor the second application to better meet user needs, such as by adapting interfaces, recommendations, or workflows based on observed behavior in the first application. The invention may also include mechanisms for securely transferring and storing the captured data, ensuring privacy and compliance with relevant regulations. The overall goal is to leverage insights from one application to enhance the user experience in another, improving efficiency and engagement.
16. A system for providing search suggestions, the system comprising: one or more processors; and a memory communicatively coupled to the one or more processors, wherein the memory comprises instructions which, when executed by the one or more processors, cause the one or more processors to perform a method comprising the steps of: capturing, by a computer, data from a first application, wherein the data includes data displayed on a screen by the first application; segmenting, by the computer, the data from the first application into blocks, wherein the blocks correspond to areas displayed on the screen; extracting, by the computer, words from the data from the first application; generating, by the computer, tokens of phrases and singular words from the extracted words using natural language processing tokenization; retrieving, by the computer, stored significance data for the first application, wherein the significance data includes information used for determining respective blocks that will be more likely to result in a search; scoring, by the computer, the blocks based upon the data from the first application, wherein the scoring of the blocks is further based upon the stored significance data; scoring, by the computer, the tokens based upon the scoring of the blocks and the data from the first application; and detecting, by the computer, a switch action from the first application to a second application, wherein the second application contains a search function; providing, by the computer, the scored tokens to the search function of the second application.
The system provides search suggestions by analyzing content from a first application and transferring relevant terms to a second application with a search function. The system captures data displayed on the screen of the first application and segments it into blocks corresponding to visible areas. Words are extracted from the data, and natural language processing techniques generate tokens from phrases and individual words. The system retrieves stored significance data for the first application, which indicates which blocks are more likely to contain searchable content. Blocks are scored based on their content and the significance data, and tokens are scored based on the block scores and the extracted data. When a user switches from the first application to the second application, the system provides the scored tokens to the search function of the second application, enabling quick and context-aware search suggestions. This approach improves search efficiency by leveraging contextual information from the source application and prioritizing relevant terms for search queries.
17. The system of claim 16 , wherein the data captured from the first application further includes data on user behavior in the first application.
This invention relates to a system for integrating data from multiple applications to enhance user experience or system functionality. The system captures data from a first application, including user behavior data such as interactions, usage patterns, or preferences within that application. This data is then processed and used to inform or influence operations in a second application. The system may also include mechanisms to analyze the captured data, such as identifying trends or correlations, and may apply machine learning or other analytical techniques to derive insights. The integration between the applications may involve real-time data transfer, synchronization, or conditional logic to trigger actions in the second application based on the first application's data. The system may be used in various domains, such as productivity tools, enterprise software, or user experience optimization, where cross-application data sharing improves efficiency, personalization, or automation. The invention addresses challenges in siloed application data, enabling more cohesive and intelligent interactions across different software environments.
18. The system of claim 16 , wherein the stored significance data includes prior determinations of blocks for the first application.
A system is provided for managing data blocks in a computing environment, particularly for applications that require efficient storage and retrieval of significant data. The system addresses the challenge of optimizing storage and processing by tracking and prioritizing data blocks based on their significance to a first application. The system includes a storage device that stores significance data, which includes prior determinations of which blocks are important for the first application. This allows the system to dynamically adjust storage and retrieval operations to focus on the most relevant data blocks, improving performance and reducing resource usage. The system may also include a processor that processes the significance data to determine the importance of different blocks, enabling the system to prioritize operations accordingly. By maintaining historical significance data, the system can learn from past interactions and adapt to the evolving needs of the application, ensuring that critical data is always accessible while minimizing unnecessary storage and processing overhead. This approach is particularly useful in environments where data volume is large and performance is critical, such as in enterprise applications or real-time systems.
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March 10, 2020
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